Optimize with Hyperactive¶

Home

Install¶

In [ ]:

!pip install hyperactive

!pip install hyperactive

Sample strategy¶

In [1]:

import talib.abstract as ta

from lettrade import DataFeed, Strategy, crossover, crossunder
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest


class SmaCross(Strategy):
    ema1_period = 9
    ema2_period = 21

    def indicators(self, df: DataFeed):
        df["ema1"] = ta.EMA(df, timeperiod=self.ema1_period)
        df["ema2"] = ta.EMA(df, timeperiod=self.ema2_period)

        df["signal_ema_crossover"] = crossover(df.ema1, df.ema2)
        df["signal_ema_crossunder"] = crossunder(df.ema1, df.ema2)

    def next(self, df: DataFeed):
        if len(self.orders) > 0 or len(self.trades) > 0:
            return

        if df.signal_ema_crossover[-1]:
            price = self.data.close[-1]
            self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001)
        elif df.signal_ema_crossunder[-1]:
            price = self.data.close[-1]
            self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001)


lt = let_backtest(
    strategy=SmaCross,
    datas="example/data/data/EURUSD_5m_0_10000.csv",
    account=ForexBackTestAccount,
)

import talib.abstract as ta from lettrade import DataFeed, Strategy, crossover, crossunder from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest class SmaCross(Strategy): ema1_period = 9 ema2_period = 21 def indicators(self, df: DataFeed): df["ema1"] = ta.EMA(df, timeperiod=self.ema1_period) df["ema2"] = ta.EMA(df, timeperiod=self.ema2_period) df["signal_ema_crossover"] = crossover(df.ema1, df.ema2) df["signal_ema_crossunder"] = crossunder(df.ema1, df.ema2) def next(self, df: DataFeed): if len(self.orders) > 0 or len(self.trades) > 0: return if df.signal_ema_crossover[-1]: price = self.data.close[-1] self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001) elif df.signal_ema_crossunder[-1]: price = self.data.close[-1] self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001) lt = let_backtest( strategy=SmaCross, datas="example/data/data/EURUSD_5m_0_10000.csv", account=ForexBackTestAccount, )

Example¶

In [2]:

from hyperactive import Hyperactive
from hyperactive.optimizers import HillClimbingOptimizer


# define the model in a function
def params_parser(args):
    params = [
        ("ema1_period", int(args["ema1_period"])),
        ("ema2_period", int(args["ema2_period"])),
    ]
    return params


def result_parser(result):
    return result["Equity [$]"]


model = lt.optimize_model(
    params_parser=params_parser,
    result_parser=result_parser,
    fork_data=True,
)

# search space determines the ranges of parameters you want the optimizer to search through
search_space = {
    "ema1_period": list(range(5, 25, 1)),
    "ema2_period": list(range(10, 50)),
}

optimizer = HillClimbingOptimizer(epsilon=0.1, distribution="laplace", n_neighbours=4)

# start the optimization run
hyper = Hyperactive()
hyper.add_search(model, search_space, optimizer=optimizer, n_iter=1000)
hyper.run()

from hyperactive import Hyperactive from hyperactive.optimizers import HillClimbingOptimizer # define the model in a function def params_parser(args): params = [ ("ema1_period", int(args["ema1_period"])), ("ema2_period", int(args["ema2_period"])), ] return params def result_parser(result): return result["Equity [$]"] model = lt.optimize_model( params_parser=params_parser, result_parser=result_parser, fork_data=True, ) # search space determines the ranges of parameters you want the optimizer to search through search_space = { "ema1_period": list(range(5, 25, 1)), "ema2_period": list(range(10, 50)), } optimizer = HillClimbingOptimizer(epsilon=0.1, distribution="laplace", n_neighbours=4) # start the optimization run hyper = Hyperactive() hyper.add_search(model, search_space, optimizer=optimizer, n_iter=1000) hyper.run()

[0] _optimize_model (Hill Climbing): 100%|──────────| 1000/1000 [04:26<00:00,  3.75it/s, best_iter=0, best_pos=[18  3], best_score=1169.0]

Results: '_optimize_model'  
   Best score: 1169.0  
   Best parameter set:
      'ema1_period' : 23.0  
      'ema2_period' : 13.0  
   Best iteration: 0  
 
   Random seed: 742903516  
 
   Evaluation time   : 265.45777797698975 sec    [99.95 %]
   Optimization time : 0.12505793571472168 sec    [0.05 %]
   Iteration time    : 265.58283591270447 sec    [3.77 iter/sec]
 

In [3]:

df = hyper.search_data(model)
df

df = hyper.search_data(model) df

Out[3]:

	ema1_period	ema2_period	score
0	23	45	1008.3
1	10	20	982.3
2	11	23	851.7
3	11	36	940.7
4	17	23	959.7
...	...	...	...
995	24	19	959.1
996	15	39	984.3
997	22	39	921.7
998	17	14	1056.1
999	23	12	1158.4

1000 rows × 3 columns

Plot¶

In [5]:

import plotly.io as pio

pio.renderers.default = "notebook"

import plotly.io as pio pio.renderers.default = "notebook"

Type 1¶

In [6]:

import plotly.express as px

fig = px.density_heatmap(
    df,
    x="ema1_period",
    y="ema2_period",
    z="score",
    nbinsx=30,
    nbinsy=30,
    histfunc="max",
    color_continuous_scale="Viridis",
)
fig.show()

import plotly.express as px fig = px.density_heatmap( df, x="ema1_period", y="ema2_period", z="score", nbinsx=30, nbinsy=30, histfunc="max", color_continuous_scale="Viridis", ) fig.show()

Type 2¶

In [7]:

import plotly.express as px

fig = px.density_contour(
    df,
    x="ema1_period",
    y="ema2_period",
    z="score",
    histfunc="max",
)
fig.update_traces(contours_coloring="fill", contours_showlabels=True)
fig.show()

import plotly.express as px fig = px.density_contour( df, x="ema1_period", y="ema2_period", z="score", histfunc="max", ) fig.update_traces(contours_coloring="fill", contours_showlabels=True) fig.show()